A generalized method for refining and selecting random crystal structures using graph theory
Abstract
Random generation of crystal structures is a key to the success of predicting unknown crystals. In this work, we introduce a general method for refining and selecting random structures that relies on minimal prior information. The method establishes a quotient graph from the random structure using a near-neighbor finding algorithm, which subsequently guides the refinement of the initial structure. To validate this approach, we apply it to nine distinct systems, and the outcomes indicate that it effectively yields a great number of low-energy structures. This technique could be integrated into most structure prediction algorithms to generate more sound initial structures, thereby expediting the search for ground state structures.
Article Details
Journal Info
The Journal of Chemical Physics
American Institute of Physics
Authors (9)
Shaobo Yu
Junjie Wang
State Key Laboratory of Quantum Functional Materials, School of Physical Science and Technology
Yu Han
Qiuhan Jia
Zhixin Liang
National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093,
Ziyang Yang
National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093,
Yujian Pan
National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University , Nanjing 210093,
Hao Gao
Institute of Traditional Chinese Medicine and Natural Products, College of Pharmacy/State Key Laboratory of Bioactive Molecules and Druggability Assessment/International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Development of Chinese Ministry of Education of China/Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research
Jian Sun